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Machine Learning Methods in the Environmental Sciences

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Release : 2009-07-30
Genre : Computers
Kind : eBook
Book Rating : 928/5 ( reviews)

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Book Synopsis Machine Learning Methods in the Environmental Sciences by : William W. Hsieh

Download or read book Machine Learning Methods in the Environmental Sciences written by William W. Hsieh. This book was released on 2009-07-30. Available in PDF, EPUB and Kindle. Book excerpt: A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

Machine Learning Methods in the Environmental Sciences

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Release : 2014-05-14
Genre : Environmental sciences
Kind : eBook
Book Rating : 526/5 ( reviews)

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Book Synopsis Machine Learning Methods in the Environmental Sciences by : William Wei Hsieh

Download or read book Machine Learning Methods in the Environmental Sciences written by William Wei Hsieh. This book was released on 2014-05-14. Available in PDF, EPUB and Kindle. Book excerpt: A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

Artificial Intelligence Methods in the Environmental Sciences

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Release : 2008-11-28
Genre : Science
Kind : eBook
Book Rating : 192/5 ( reviews)

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Book Synopsis Artificial Intelligence Methods in the Environmental Sciences by : Sue Ellen Haupt

Download or read book Artificial Intelligence Methods in the Environmental Sciences written by Sue Ellen Haupt. This book was released on 2008-11-28. Available in PDF, EPUB and Kindle. Book excerpt: How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Machine Learning for Spatial Environmental Data

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Release : 2009-06-09
Genre : Science
Kind : eBook
Book Rating : 376/5 ( reviews)

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Book Synopsis Machine Learning for Spatial Environmental Data by : Mikhail Kanevski

Download or read book Machine Learning for Spatial Environmental Data written by Mikhail Kanevski. This book was released on 2009-06-09. Available in PDF, EPUB and Kindle. Book excerpt: Acompanyament de CD-RM conté MLO software, la guia d'MLO (pdf) i exemples de dades.

Deep Learning for the Earth Sciences

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Release : 2021-08-18
Genre : Technology & Engineering
Kind : eBook
Book Rating : 162/5 ( reviews)

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Book Synopsis Deep Learning for the Earth Sciences by : Gustau Camps-Valls

Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls. This book was released on 2021-08-18. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

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